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All Journal Journal of Holistic Medical Technologies
Hanin Aisya Fakihati
Universitas Islam Negeri Walisongo Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Neuroscience

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Classification of gastric tissue images based on texture characteristics using the Random Forest method Hesti Windyasari; Putri Zulfikah; Hanin Aisya Fakihati; Nabila Triwahyuni Handayani; Fitria Kholbi Azizah; Wahyu Malda Sere
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 1 (2024): December
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Gastric cancer is a group of malignant diseases caused by many factors, including genetics, lifestyle, and environment. This study aims to create additional tools for distinguishing gastric cancer and normal in microphysical biopsy images from the Kaggle database; the dataset includes 98 gastric cancer and 95 normal. The method used in this research utilizes the coarse and delicate nature of the extracted image based on Histogram and Gray Level Co-occurrence Matrix (GLCM) texture features. Image classification uses the Random Forest method in WEKA software. The results showed that the highest accuracy was 94% in folds 15, 20, and 25, while the lowest accuracy was 93% in folds 5 and 10. This research can be an additional tool for differentiating microphysical biopsy images.
Electrical Capacitance Volume Tomography (ECVT) for real-time brain activity monitoring: a comparative frequency analysis study Hanin Aisya Fakihati; Seftina Diyah Miasary; Marlin Ramadhan Baidillah
Journal of Holistic Medical Technologies (JHMT) Vol. 1 No. 2 (2025): June
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Current brain imaging modalities such as CT scan and MRI, while providing excellent anatomical detail, have limitations in real-time functional brain activity monitoring. Electrical Capacitance Volume Tomography (ECVT) emerges as a promising non-invasive, cost-effective alternative for dynamic brain activity assessment. This study aims to evaluate the sensitivity of ECVT technology in detecting brain motor activity variations across different frequencies and determine the optimal frequency for brain wave fluctuation measurement. A 16-electrode ECVT helmet system was employed to monitor brain activity in subjects performing motor stimulation tasks including hand gripping, imagined movement, and control conditions (water and empty space). Measurements were conducted at three frequency variations: 500 kHz, 1 MHz, and 5 MHz. Data acquisition involved multiple channel combinations (C14-16, C14-15, C14-13, C14-12, C16-15, C16-9, C16-8, C16-10) with voltage peak-to-peak (Vpp) measurements recorded via oscilloscope. The 500 kHz frequency demonstrated the highest sensitivity in detecting brain activity variations. Distinct Vpp patterns were observed across different motor tasks, with imagined movement producing the highest values, indicating increased neural activity. The ECVT system successfully differentiated between active motor tasks and resting states. ECVT at 500 kHz frequency shows superior sensitivity for brain activity monitoring, offering a portable, low-cost alternative to conventional neuroimaging modalities for real-time functional brain assessment.
Co-Authors Fitria Kholbi Azizah Hesti Windyasari Marlin Ramadhan Baidillah Nabila Triwahyuni Handayani Putri Zulfikah Seftina Diyah Miasary Wahyu Malda Sere